def neh(data, nb_machines, nb_jobs):
order_seq = order_neh(data, nb_machines, nb_jobs)
seq_current = [order_seq[0]]
for i in range(1, nb_jobs):
min_cmax = float("inf")
for j in range(0, i + 1):
tmp_seq = insertion(seq_current, j, order_seq[i])
cmax_tmp = commonFunction.makespan(
tmp_seq, data, nb_machines)[nb_machines - 1][len(tmp_seq)]
print(tmp_seq, cmax_tmp)
if min_cmax > cmax_tmp:
best_seq = tmp_seq
min_cmax = cmax_tmp
seq_current = best_seq
return seq_current, commonFunction.makespan(
seq_current, data, nb_machines)[nb_machines - 1][nb_jobs]
def neh(data, nb_machines, nb_jobs):
order_seq = order_neh(data, nb_machines, nb_jobs)
#Ustawiamy task z najwyższą sumą jako pierwszy element listy
seq_current = [order_seq[0]]
for i in range(1, nb_jobs):
#Zainicjowanie wartości minimalnej jako infinity
min_cmax = float("inf")
for j in range(0, i + 1):
tmp_seq = insertion(seq_current, j, order_seq[i])
cmax_tmp = commonFunction.makespan(
tmp_seq, data, nb_machines)[nb_machines - 1][len(tmp_seq)]
#print(tmp_seq, cmax_tmp)
if min_cmax > cmax_tmp:
best_seq = tmp_seq
min_cmax = cmax_tmp
seq_current = best_seq
return seq_current, commonFunction.makespan(
seq_current, data, nb_machines)[nb_machines - 1][nb_jobs]
def cds(data, nb_machines, nb_jobs):
c_max_best = float("inf")
for k in range(1, nb_machines):
p1_prime = get_merge(0, k, data, nb_jobs)
p2_prime = get_merge(nb_machines - k, nb_machines, data, nb_jobs)
ss_pb = [p1_prime, p2_prime]
print(ss_pb)
current_seq = commonFunction.u(ss_pb, nb_jobs) + commonFunction.v(
ss_pb, nb_jobs)
c_max = commonFunction.makespan(current_seq, data,
nb_machines)[nb_machines - 1][nb_jobs]
print(current_seq, c_max)
if c_max < c_max_best:
c_max_best = c_max
best_seq = current_seq
return best_seq, c_max_best
def cds(data, nb_machines, nb_jobs):
c_max_best = float("inf")
for k in range(1, nb_machines):
p1_prime = get_merge(0, k, data, nb_jobs)
p2_prime = get_merge(nb_machines - k, nb_machines, data, nb_jobs)
ss_pb = [p1_prime, p2_prime]
print(ss_pb)
current_seq = commonFunction.u(ss_pb, nb_jobs) + commonFunction.v(
ss_pb, nb_jobs)
c_max = commonFunction.makespan(current_seq, data,
nb_machines)[nb_machines - 1][nb_jobs]
print(current_seq, c_max)
if c_max < c_max_best:
c_max_best = c_max
best_seq = current_seq
return best_seq, c_max_best
# run CDS
nbm, nbj, p_ij = commonFunction.read_from_file("example2.txt")
seq, cmax = cds(p_ij, nbm, nbj)
print("nbMachines:", nbm)
print("nbJobs:", nbj)
print("data: p_ij, the processing time of jth job on ith machine\n", p_ij)
print("cds:", seq, cmax)
interface.graphic("CDS", seq, nbj, nbm,
commonFunction.makespan(seq, p_ij, nbm), p_ij)
def random_seq(data, num_of_task, nb_mach):
seq = list(range(0, num_of_task))
random.shuffle(seq)
time_of_seq = commonFunction.makespan(seq, data,
nb_mach)[nb_mach - 1][len(seq)]
return seq, time_of_seq
#Zainicjowanie wartości minimalnej jako infinity
min_cmax = float("inf")
for j in range(0, i + 1):
tmp_seq = insertion(seq_current, j, order_seq[i])
cmax_tmp = commonFunction.makespan(
tmp_seq, data, nb_machines)[nb_machines - 1][len(tmp_seq)]
#print(tmp_seq, cmax_tmp)
if min_cmax > cmax_tmp:
best_seq = tmp_seq
min_cmax = cmax_tmp
seq_current = best_seq
return seq_current, commonFunction.makespan(
seq_current, data, nb_machines)[nb_machines - 1][nb_jobs]
# run NEH
time_table = np.array(data1).transpose()
seq, cmax = neh(time_table, num_of_divices, num_of_tasks)
print("nbMachines:", num_of_divices)
print("nbJobs:", num_of_tasks)
#print("data: p_ij, the processing time of jth job on ith machine\n", time_table)
print("neh: ", seq, cmax)
#print(cmax)
interface.graphic("NEH", seq, num_of_tasks, num_of_divices,
commonFunction.makespan(seq, time_table, num_of_divices),
time_table)
seque, t = sym_wyz(time_table, num_of_tasks, num_of_divices)
print("symul: ", seque, t)
matplotlib.pyplot.show()
def johnson(data, nb_machines, nb_jobs):
seq_current = commonFunction.u(data, nb_jobs) + commonFunction.v(data, nb_jobs)
return seq_current, commonFunction.makespan(seq_current, p_ij, nb_machines)[nb_machines - 1][nb_jobs]
import commonFunction
import interface
def johnson(data, nb_machines, nb_jobs):
seq_current = commonFunction.u(data, nb_jobs) + commonFunction.v(data, nb_jobs)
return seq_current, commonFunction.makespan(seq_current, p_ij, nb_machines)[nb_machines - 1][nb_jobs]
# run Johnson
nbm, nbj, p_ij = commonFunction.read_from_file("example.txt")
seq, cmax = johnson(p_ij, nbm, nbj)
print("nbMachines:", nbm)
print("nbJobs:", nbj)
print("data: p_ij, the processing time of jth job on ith machine\n", p_ij)
print("johnson:", seq, cmax)
interface.graphic("Johnson", seq, nbj, nbm, commonFunction.makespan(seq, p_ij, nbm), p_ij)